• 한국압력기기공학회 논문집
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• 제10권1호
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• pp.64-69
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• 2014

Gr. 91 steel is used for the major structural components of Generation-IV reactor systems, such as a very high temperature reactor(VHTR) and sodium-cooled fast reactor(SFR). Since these structures are designed for up to 60 years at elevated temperatures, the prediction of long-term creep life is important for a design application of Gr. 91 steel. In this study, a number of creep rupture data were collected through world-wide literature surveys, and using these data, the long-term creep life was predicted in terms of three methods: the single-C method in Larson-Miller(L-M) parameter, multi-C constant method in the L-M parameter, and a modified method("sinh" equation) in the L-M parameter. The results of the creep-life prediction were compared using the standard deviation of error value, respectively. Modified method proposed by the "sinh" equation revealed better agreement in creep life prediction than the single-C L-M method.

• Structural Engineering and Mechanics
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• 제53권5호
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• pp.921-938
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• 2015

Realistic prediction of concrete creep is of crucial importance for durability and long-term serviceability of concrete structures. To date, research about the behaviour of self-compacting concrete (SCC) members, especially concerning the long-term performance, is rather limited. SCC is quite different from conventional concrete (CC) in mixture proportions and applied materials, particularly in the presence of aggregate which is limited. Hence, the realistic prediction of creep strains in SCC is an important requirement for the design process of this type of concrete structures. This study reviews the accuracy of the conventional concrete (CC) creep prediction models proposed by the international codes of practice, including: CEB-FIP (1990), ACI 209R (1997), Eurocode 2 (2001), JSCE (2002), AASHTO (2004), AASHTO (2007), AS 3600 (2009). Also, SCC creep prediction models proposed by Poppe and De Schutter (2005), Larson (2007) and Cordoba (2007) are reviewed. Further, new creep prediction model based on the comprehensive analysis on both of the available models i.e. the CC and the SCC is proposed. The predicted creep strains are compared with the actual measured creep strains in 55 mixtures of SCC and 16 mixtures of CC.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• 제어로봇시스템학회논문지
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• 제18권11호
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• pp.989-996
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• 2012

The purpose of this paper is to analyze GPS (Global Positioning System) satellite orbital mechanics, and then to propose a novel long-term GPS satellite orbit prediction scheme including virtual planet perturbation. The GPS orbital information is a necessary prerequisite to pinpointing the location of a GPS receiver. When a GPS receiver has been shut down for a long time, however, the time needed to fix it before its reuse is too long due to the long-standing GPS orbital information. To overcome this problem, the GPS orbital mechanics was studied, such as Newton's equation of motion for the GPS satellite, including the non-spherical Earth effect, the luni-solar attraction, and residual perturbations. The residual perturbations are modeled as a virtual planet using the least-square algorithm for a moment. Through the modeling of the virtual planet with the aforementioned orbital mechanics, a novel GPS orbit prediction scheme is proposed. The numerical results showed that the prediction error was dramatically reduced after the inclusion of virtual planet perturbation.

• Steel and Composite Structures
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• 제39권1호
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• pp.21-33
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• 2021

This paper aims to improve the current state-of-the-art in long-term deflection prediction in steel-concrete composite beams. The efficiency of a time-dependent finite element model based on linear creep theory is verified with available experimental data. A parametric numerical study is then carried out, which focuses on the effects of concrete creep and/or shrinkage, ultimate shrinkage strain and reinforcing bars in the slab. The study shows that the long-term deformations in composite beams are dominated by concrete shrinkage and that a higher area of reinforcing bars leads to lower long-term deformations and steel stresses. The AISC model appears to overestimate the shrinkage-induced deflection. A modified ACI equation is proposed to quantify time-dependent deflections in composite beams. In particular, a modified reduction factor reflecting the influence of reinforcing bars and a coefficient reflecting the influence of ultimate shrinkage are introduced in the proposed equation. The long-term deflections predicted by this equation and the results of extensive numerical analyses are found to be in good agreement.

• Smart Structures and Systems
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• 제29권3호
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• pp.433-444
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• 2022

Concrete is a most utilized material in the construction industry that have main components. The strength of concrete can be improved by adding some admixtures. Evaluating the impact of fly ash (FA) and silica fume (SF) on the long-term compressive strength (CS) of concrete provokes to find the significant parameters in predicting the CS, which could be useful in the practical works and would be extensible in the future analysis. In this study, to evaluate the effective parameters in predicting the CS of concrete containing admixtures in the long-term and present a fitted equation, the multivariate adaptive regression splines (MARS) method has been used, which could find a relationship between independent and dependent variables. Next, for optimizing the output equation, biogeography-based optimization (BBO), particle swarm optimization (PSO), and hybrid PSOBBO methods have been utilized to find the most optimal conclusions. It could be concluded that for CS predictions in the long-term, all proposed models have the coefficient of determination (R²) larger than 0.9243. Furthermore, MARS-PSOBBO could be offered as the best model to predict CS between three hybrid algorithms accurately.

• 한국공간구조학회논문집
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• 제14권3호
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• pp.93-100
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• 2014

Ethylene Tetrafluoroethylene (ETFE) has been widely used in long-span buildings because of its light weight and high transparency. This paper studies the short and long term creep behaviour of ETFE foil. A series of short-term creep and recovery tests were performed, in which the residual strain was observed. A long-term creep test of the ETFE foil was also performed over 110 days. A viscoelastic-plastic model was then established to describe the short-term creep and recovery behaviour. The model contains a traditional multi-Kelvin part and an added steady-flow component to represent the viscoelastic and viscoplastic behaviour, respectively. The model successfully fit the data for three stresses and six temperatures. Additionally, time-temperature equivalency was adopted to predict the long-term creep behaviour of ETFE foil. Horizontal shifting factors were determined from the process of shifting creep-curves at six temperatures. The long-term creep behaviours at three temperatures were predicted. Finally, the long-term creep test showed that the short-term creep test at identical temperatures insufficiently predicted additional creep behaviour, and the long-term test verified the horizontal shifting factors derived from the time-temperature equivalency.

• Computers and Concrete
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• 제31권2호
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• pp.85-95
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• 2023

Natural pozzolans are used as additives in cement to develop more durable and high-performance concrete. Pozzolanic activity index (PAI) is important for assessing the performance of a pozzolan as a binding material and has an important effect on the compressive strength, permeability, and chemical durability of concrete mixtures. However, the determining of the 28 days (short term) and 90 days (long term) PAI of concrete mixtures is a time-consuming process. In this study, to reduce extensive experimental work, it is aimed to predict the short term and long term PAIs as a function of the chemical compositions of various natural pozzolans. For this purpose, the chemical compositions of various natural pozzolans from Central Anatolia were determined with X-ray fluorescence spectroscopy. The mortar samples were prepared with the natural pozzolans and then, the short term and the long term PAIs were calculated based on compressive strength method. The effect of the natural pozzolans' chemical compositions on the short term and the long term PAIs were evaluated and the PAIs were predicted by using multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) model. The prediction model results show that both reactive SiO₂ and SiO₂+Al₂O₃+Fe₂O₃ contents are the most effective parameters on PAI. According to the performance of prediction models determined with metrics such as root mean squared error (RMSE) and coefficient of correlation (R²), ANFIS models are more feasible than the multiple regression model in predicting the 28 days and 90 days pozzolanic activity. Estimation of PAIs based on the chemical component of natural pozzolana with high-performance prediction models is going to make an important contribution to material engineering applications in terms of selection of favorable natural pozzolana and saving time from tedious test processes.

• ETRI Journal
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• 제44권2호
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• pp.208-219
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• 2022

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

• 한국멀티미디어학회논문지
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• 제21권2호
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• pp.271-280
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• 2018

Recently, big data has been growing rapidly due to the development of IT technology. Especially in the medical field, big data is utilized to provide services such as patient-customized medical care, disease management and disease prediction. In Korea, 'National Health Alarm Service' is provided by National Health Insurance Corporation. However, the prediction model has a problem of short-term prediction within 3 days and unreliability of social data used in prediction model. In order to solve these problems, this paper proposes a disease prediction model using medicine prescription data generated from actual patients. This model predicts the total number of patients and the risk of disease in each region and uses the ARIMA model for long-term predictions.